Senior Machine Learning Engineer

3 weeks ago

Chennai, Tamil Nadu, India embedUR systems Full time
We're seeking a Senior Machine Learning Engineer with deep expertise in building and deploying ML models optimized for edge devices. This role is ideal for someone passionate about pushing the boundaries of AI on resource-constrained hardware, across domains such as computer vision, audio analytics, and sensor-based signal processing.

Key Responsibilities

- Design, train, and optimize deep learning models for edge AI use cases:
- Computer vision (e.g., object detection, image classification, segmentation)
- Audio/speech processing (e.g., wake word detection, denoising)
- Signal processing across multimodal sensor data
- Develop scalable and efficient model training and evaluation pipelines.
- Quantize, prune, and compress models for real-time inference on NPUs, DSPs, and microcontrollers.
- Conduct applied research and benchmark new model architectures or techniques for edge deployment.
- Prepare and curate training/validation datasets using best practices for data quality and balance.
- Collaborate with embedded software engineers to integrate models into edge production environments.
- Monitor model performance, perform error analysis, and iterate for continuous improvement.
- Document model architectures, assumptions, and deployment requirements for stakeholders.

Qualifications

- Master's degree (or equivalent experience) in Computer Science, Machine Learning, or a related discipline.
- 5+ years of hands-on experience in developing, optimizing, and deploying ML models in production.
- Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, ONNX, and tools like OpenCV, Ultralytics, or equivalent.
- Demonstrated experience with edge inference optimization:
- Model quantization and compression
- Deployment on NPUs, DSPs, or microcontrollers
- Familiarity with ARM Ethos-U/Vela, TensorRT, TFLite, or similar edge AI compilers is a strong plus.
- Solid understanding of signal processing for image, audio, and other sensor modalities.
- Experience with cloud-based training pipelines (AWS, GCP, or Azure) is a plus.
- Strong analytical and problem-solving skills, with a track record of innovation.
- Excellent communication and documentation skills.

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